KnE Social Sciences | 3rd UNJ International Conference on Technical and Vocational Education and Training 2018 (3rd ICTVET 2018) | pages: 645–652

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1. Introduction

"There are at least three main conditions that must be considered in the development of education in order to contribute to the improvement of the quality of Human Resources (HR), namely: 1) building facilities, 2) quality books, 3) teachers and professional education personnel" (E. Mulyasa, 2011: 3).

According to Sardiman (2014: 19), in the teaching and learning process, teachers as teachers and students as subjects of learning, required a certain qualification profile `in terms of knowledge, abilities, attitudes and nature of the nature of its properties, Effective and efficient.

According to Theodore Coladarci, “the greater teaching commitment of the teachers who were higher in both general and personal efficacy; who is taught in schools with fewer students per teacher; and who worked under a principal of positive in the areas of instructional leadership, school advocacy, decision making, and relations with students and staff“[7].

This teaching commitment, by Kusman (Dahlan, 2008: 67) is called "commitment to student learning" which reflects the dedication of teachers in helping students to learn. Based on the exposure it can be a reference, teaching commitment can affect student learning outcomes.

Departing from the background, the authors are interested to learn about the influence of teachers and commitment to the students' learning outcomes of Machining Engineering Program. Objectives to be achieved from this research is information about: (1) Knowing the influence of teachers on student learning outcomes; (2) Knowing the influence of commitment to student learning outcomes; (3) Know the influence of teachers and commitment to student learning outcomes.

2. Methods

The research was conducted in April 2017-June 2017 at Grade 10 of Machining Technique and Productive Teacher at Vocational School of State 34 Jakarta Pusat which is located at Jalan Kramat Raya Number 93, Salemba, Senen, Central Jakarta. The method used in this research is associative method. The population in this research is all students of Machining Technique, while the sample under study used 31 students of 10th grade of Machining Technique and Productive Teacher as many as 12 people.

In this study, independent variables (X1 and X2) use secondary data technique with questionnaire. And for the dependent variable (Y) use the primary data of learning result of even semester. Data analysis technique used in this research is divided into two, namely prerequisite test using data of normality using Chi Square; And hypothesis test consisting of simple regression test, multiple regression, and F test.

3. Results

Data normality test

Table 1

The value of Chi Square


Teachers Competency Commitment Teaching Student Learning Outcomes
Dk=n-1 6 5 6
Value X 2 table 12,6 11,07 12,6
Value X 2 count 11,97 4,94 4,13

In table 1. The value of chi square, X 2 table = 12.6 for α = 0.05 and df = 6. Because the

X 2 count < X 2 table it can be concluded that the dissemination of data on all variables are normally distributed.

Data of teacher competence variable (X1)

Figure 1

Histogram Variable Teacher Competence.

fig-1.jpg
Table 2

Percentage Fulfillment Teacher Competence.


Variable Indicator Total questiu ns score ideal Total % fulfillment indicator Total score ideal score % variable fulfillment
Teacher Competence Pedagogical Competence 7 868 468 53,91 1368 2976 45,96
Personality Competence 8 992 522 52,62
Social Competence 4 496 70 14,11
Professional Competence 5 620 308 49,67
Table 3

Average Score Calculate variable indicator X1 Teacher Competence.


Variable Indicator Number Problem amount Item Problem Average Score Total av erage Score %
Teacher Competence Pedagogical Competence 7 468 66,86 211,2 31,65
Personality Competence 8 522 65,25 30,89
Social Competence 4 70 17,5 8,28
Professional Competence 5 308 61,6 29,16

Data of teaching commitment variable (X2)

Figure 2

Histogram Variables Teaching Commitment.

fig-2.jpg
Table 4

Percentage of the fulfillment of the Teaching Commitment.


variable Indicator Total Problem Score Ideal Total Score % fulfillment indicator total score ideal score Total % fulfillment variables
Teaching profession 16 768 317 41.3 748 16 44.5
humanity 16 768 358 46.6
Commitment 80
Society 3 144 73 50.69
Table 5

Average Score Calculate Variable Indicator X2 Teaching Commitment.


Variables Indicators Number Problem Item Problem Number Average Score Number average Score %
Teaching profession 16 317 19.81 66.52 29.78
humanity 16 358 22.37 33.63
Commitment
Society 3 73 24.33 36.58

Data of variable student learning outcomes (Y)

Figure 3

Histogram variable Student Learning Outcomes.

fig-3.jpg
Table 6

Percentage of fulfillment variable Student Learning Outcomes.


Variable Total Score Score Ideal fulfillment variable %
Student Learning Outcomes 14771 18600 79,41
Table 7

Average Score Calculate variable Y indicator Student Learning Outcomes.


variable Indicators Students Total Value Average Value Total Average Value %
Student Learning Outcomes Under the criteria (75) 0 0 0 2461,8 0
Above the criteria (75) 31 14771 2461,8 100

4. Discussion

Analysis of effect teacher competence (X1) of the student learning outcomes (Y)

The research hypothesis is that there is a positive and significant influence of teacher competence on the result of the Machining engineering skills program.

Based on the calculation, the known value of the count r = 0.372 df = 31-1 = 30 for = 5% with r table = 0.361. Because the count r r table, we conclude that H 1 is accepted. From the test results F count (17.15) F table (3.32), then there is a significant influence between the variables X 1 and variable Y. Can be concluded that the contribution of student achievement is determined by the competence of teachers was 13.8% and the rest 86.2% is determined by other variables.

Simple linear regression analysis to pair of research data between teacher competence of student learning achievement yield regression coefficient (b) equal to -0,49 and constant (a) 301,70. From the calculation, the regression equation used to predict student achievement based on the teacher's competence is Ŷ = 301,70- 0,49X.

Analysis of effect teaching commitment (X2) of the student learning outcomes (Y)

The hypothesis of the research is that there is a positive and significant influence of students 'learning environment on the students' learning outcomes of Machining Engineering Program.

Based on the calculation, the known value of the count r = 0.127 df = 12-1 = 11 for = 5% with r table = 0.602. Because the count r r table, we conclude that H 0 is rejected. From the test results calculated F (1,44) F table (3.98), then there is a significant difference between the variables X 2 and variable Y. It can be concluded that the contribution of student achievement determined by the commitment of 1.6% and The remaining 98.4% is determined by other variables.

Simple linear regression analysis to the pair of research data between the commitment of teaching on student learning result yield regression coefficient (b) equal to -0,26 and constant (a) 185,34. From the calculation, the regression equation used to predict student achievement based on students' learning environment is Ŷ = 185,34-0,26 X.

Analysis of effect teacher competence (X1) and teaching commitment (X2) of the student learning outcomes (Y)

The research hypothesis is that there is a positive influence of teacher competence and teaching commitment to the students' learning outcomes of Machining Engineering Program.

Based on the calculation, the known value of the count r = 0.557 df = 31-1 = 30 for = 5% with r table = 0.361. Because the count r r table, we conclude that H 1 is

accepted. From the test results calculated F (5,65) F table (3.32), then there is a significant influence between the variables X 1 and X 2 to variable Y. It can be concluded that the contribution of student learning outcomes determined by the competence and commitment of teachers to teach at 31.02% and the remaining 68.98% is determined by other variables.

Multiple regression analysis toward pair of research data between teacher competence and teaching commitment to student learning result yield regression coefficient (c) equal to -0,91 (b) equal to -0,40 and constant (a) 558,53. From the calculation, the regression equation to predict the outcome will be used by student learning based on the competence of teachers and teaching commitment is Y = 0.91 X 1 -0,40X 558,53-x2..

5. Conclusion

Based on the objectives and research results, it can be concluded that:

  • Based on the results of the management of statistical data correlation coefficient calculation teacher competence on student learning outcomes obtained r xy at 0,363 and the 5% significance r table at 0.361. And the test results calculated F(16.55) > F table (3.32), then there is a significant influence between the variables of teacher competence and student learning outcomes variables. It shows a positive and significant effect of teacher competence in student learning outcomes light vehicle engineering expertise program. While based on the calculation of the coefficient of determination concluded that the contribution of student learning outcomes are determined by the competency of teachers by 13.21% and the remaining 86.78% is determined by other variables. To predict the results of student learning based on the competence of teachers is Y = 607.91 - 0,96X, large competence of teachers, the greater the learning outcomes of students.

  • Based on statistical data management commitment correlation coefficient calculation results of teaching the learning outcomes of students obtained r xy of 0.377 and a 5% significance r table at 0.361. And the test results calculated F (17.55) > F table (3.32), then there is a significant influence between the variables of teaching commitments and variable student learning outcomes. I showed a positive and significant influence teaching commitment to student learning outcomes technical expertise machining program. While based on the calculation of the coefficient of determination concluded that the contribution of student learning outcomes determined by the commitment to teach at 14.21% and the remaining 85.78% is determined by other variables. To predict student learning outcomes based teaching commitment is Y = 593.96 - 0,67X, the greater the commitment to teaching the greater the learning outcomes of students.

  • Based on statistical data management correlation coefficient calculation result of teacher competence and commitment to teaching the learning outcomes of students obtained xy r of 0.42 and a 5% significance r table at 0.361. And the test results calculated F (10.33) > F table (3.32), then there is a significant influence between the variables of teacher competence and commitment of teaching to student learning outcomes variables. It shows positive and significant influence teaching commitment to student learning outcomes technical expertise machining program. While based on the calculation of the coefficient of determination concluded that the contribution of student learning outcomes are determined by the competence of teachers and teaching commitments amounted to 18.02% and the remaining 81.97% is determined by other variables. To predict the results of student learning based on the competence of teachers and teaching commitment is Y = 0.52 X 615,73- 1 - 0,41X 2, the greater the competence of teachers (X-1) and a commitment to teaching (X 2) the greater student learning outcomes.

References

1 

Mulyasa. (2011). School Based Management, Concepts, Strategy and Implementation. Bandung PT Remaja Rosdakarya.

2 

Sardiman a.m. (2014). Interaction and Motivation in Teaching and Learning. Jakarta: PT Raja Grafindo Persada.

3 

Rosdiana, Dian.(2013). The Effect of Teacher Competence and Teaching Commitment on the Effectiveness of Learning Processes and Their Implications on Student Learning Outcomes Economic Subjects. Indonesian Education university, Vol. 13, pp.2.

4 

Muzayanah, Azza. (2016). Relationship between students' perceptions of pedagogical and professional competence of teachers with student learning outcomes in electrical circuit subjects: case studies at SMKN 26 Jakarta installation of technical expertise packages for the utilization of class X electricity. Thesis. State University of Jakarta.

5 

Sudjana, Nana. (2009). Assessment of Teaching and Learning Results. Bandung: PT. Remaja Rosdikarya.

6 

Sugiyono. (2007). Educational Research Methods. Bandung: Alfabeta.

7 

Theodore Coladarci. (2010). Teachers' Sense of Efficacy and Commitment to Teaching. Journal of Experimental Education, Vol. 60, pp. 323-337.

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